1. Technical Field
Example embodiments of the present invention relate to a technique of image recognition and more specifically to an image recognition apparatus and method for recognizing a subject object by configuring an effective region of the subject object in a target image.
2. Related Art
Recently, application domains of image recognition technologies and importance of them are increasing. For example, a black-box system which recognizes images by using a camera attached in a car and records them is being used as a safety device which can realize safe driving by detecting pedestrians and other cars around the car.
Generally, image recognition systems determine whether a subject object exists in an image by detecting all objects within the image. Such the method has a problem of high false detection probability and too much time required for detecting the subject object. In order to resolve such the problems, a detection method, which can detect the subject object accurately and rapidly by restricting a detection space within an image based on a geometrical relation between a camera and the ground, has been developed.
For example, in case of a fixed Closed Circuit Television (CCTV) camera attached in a building or a camera attached fixedly to a specific position of a vehicle, a robot, etc., their positions with reference to the ground usually do not change. In these cases, height, angles of pan and tilt, etc. of the camera may have geometrical relations with reference to the ground. Thus, an image recognition apparatus may exclude ineffective candidate regions from its detection space based on the geometrical relations before detecting a subject object.
FIG. 1 is a conceptual diagram illustrating image recognition.
Referring to FIG. 1, when a black-box camera attached to a car is used to detect pedestrians, ineffective candidate regions may be a region A, a region B, a region C, and so on. For example, the region A may be a region which is located near the camera but has a too small area size, and the region B may be a region which is located far from the camera but has a too large area size, and the region C may be a region located in a sky where a pedestrian usually cannot exist.
Then, the image recognition apparatus may try to detect subject objects in only effective candidate regions (e.g. a region D) by excluding the ineffective candidate regions A, B, and C so that the time required for detecting subject objects and the false detection probability may be reduced remarkably.
Specifically, in order to determine effective candidate regions within an image, a conventional image recognition apparatus is configured to estimate equations for conversion between a physical space coordinate system and an image coordinate system through a camera calibration procedure, convert candidate regions on the image coordinate system into candidate regions on the physical space coordinate system based on the equations for conversion, and determine only candidate regions whose distance and size fall within normal ranges as the effective candidate regions.
As described above, since the conventional image recognition apparatus uses a method for restricting candidate regions by using the conversion equations, there is a problem that the camera calibration procedure for deriving the conversion equations is very difficult and inconvenient.
The camera calibration may mainly comprise two steps—an internal calibration and an external calibration. The internal calibration is a procedure for calculating mechanical internal parameters of the camera itself, such as a focal distance of a lens, a distance between the lens and an image sensor, a central axis of the lens, resolution of the image sensor, etc. Also, the external calibration is a procedure for calculating external parameters related to geometrical relations between the camera and an external space, such as an installation height of the camera, angles of pan and tilt, etc.
FIG. 2 illustrates an example of a board panel utilized for a camera calibration procedure of an image recognition apparatus. Referring to FIG. 2, in the internal calibration procedure, multiple images on a board panel 20 having a form of chessboard size of which is already known are obtained, and parameter are calculated from the obtained multiple images.
FIG. 3 is a conceptual diagram to explain a camera calibration procedure performed on the spot. Referring to FIG. 3, in the external calibration procedure, the camera is equipped in a car, etc. and the calibration is performed by using a tool 30 comprising a separately-manufactured instrument.
As described above, for the conventional image recognition apparatus, the separate calibration tool 30 should be designed for each specific case, and the calibration tool 30 having a heavy weight and a big volume should be moved in a position where a target camera is installed.
Also, since an installation height of the camera and a distance between the camera and the calibration tool 30 should be inputted on the spot, there are problems that a basic knowledge of the calibration procedure and much time are necessary and it is difficult to be performed in real time.